---
title: "La Recherche publique en France"
output:
flexdashboard::flex_dashboard:
orientation: columns
vertical_layout: fill
theme: journal
social: menu
source_code: embed
---
```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(ggtext)
library(ggpubr)
library(sf)
#IMPORTATION DU JEU DE DONNEES
rdFR <- read.csv("./rd-moyens-administrations.csv", sep = ";")
#AS FACTOR
for (i in 1:12){
rdFR[,i] <- as.factor(rdFR[,i])
}
#Fond de carte France par region 2014
reg<-st_read(dsn="regions-20140306-100m-shp/regions-20140306-100m.shp")
reg$nom
#On enl?ve les DOM TOM
guadeloupe<-reg[11,]
guyane<-reg[12,]
lareunion<-reg[16,]
martinique<-reg[19,]
mayotte<-reg[20,]
reg<-reg[-c(11:12,16,19:20),]
```
Vision globale
=======================================================================
Column {data-width=650}
-----------------------------------------------------------------------
### Chart A
```{r}
```
Column {data-width=350}
-----------------------------------------------------------------------
### Chart B
```{r}
```
### Chart C
```{r}
```
La parité dans la recherche
=======================================================================
Column {data-width=650}
-----------------------------------------------------------------------
### Chart A
```{r}
par1 <- rdFR %>%
filter(code_indicateur=="pers") %>%
filter(sexe!="Non ventile") %>%
filter(annee==2001) %>%
group_by(code_region,region,annee,sexe) %>%
summarise(personnel=sum(valeur)) %>%
group_by(region) %>%
mutate(percentage=personnel/sum(personnel)*100) %>%
filter(sexe=="Femmes")
#On merge les donn?es et le fond de carte
par11<-merge(x=reg,y=par1,by.x="code_insee",by.y="code_region",all.x=T)
par2001 <- par11 %>%
st_transform( crs = 32631 )%>%
ggplot()+
geom_sf(aes(fill = as.numeric(percentage))) +
scale_fill_gradient(low = "#F1D9F7", high = "#FA28FA", breaks=c(30,35,40,45,49)) +
labs(fill = 'Percentage',title=2001)+
theme(
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks = element_blank())+
theme(plot.title = element_text(hjust = 0.5,size=10))
par2 <- rdFR %>%
filter(code_indicateur=="pers") %>%
filter(sexe!="Non ventile") %>%
filter(annee==2013) %>%
group_by(code_region,region,annee,sexe) %>%
summarise(personnel=sum(valeur)) %>%
group_by(region) %>%
mutate(percentage=personnel/sum(personnel)*100) %>%
filter(sexe=="Femmes")
#On merge les donn?es et le fond de carte
par12<-merge(x=reg,y=par2,by.x="code_insee",by.y="code_region",all.x=T)
par2013 <- par12 %>%
st_transform( crs = 32631 )%>%
ggplot()+
geom_sf(aes(fill = as.numeric(percentage))) +
scale_fill_gradient(low = "#F1D9F7", high = "#FA28FA", breaks=c(30,35,40,45,49))+
labs(fill = 'Percentage',title=2013)+
theme(
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks = element_blank())+
theme(plot.title = element_text(hjust = 0.5,size=10))
vision01_13 <- ggpubr::ggarrange(
par2001,
par2013,
nrow = 1,
ncol = 2,
common.legend = TRUE,
legend = "bottom"
)
annotate_figure(vision01_13,
top = text_grob("Proportion of women in public research", color = "black", face = "bold", size = 14),
bottom = text_grob("Data source: data.gouv",
hjust = 1, x = 1, face = "italic", size = 10)
)
```
Column {data-width=350}
-----------------------------------------------------------------------
### Chart B
```{r}
```
### Chart C
```{r}
```
Les institutions
=======================================================================
Column {data-width=650}
-----------------------------------------------------------------------
### Chart A
```{r}
```
Column {data-width=350}
-----------------------------------------------------------------------
### Chart B
```{r}
```
### Chart C
```{r}
```